Datasets:

krr-oxford
/

OntoLAMA

	# Copyright 2020 The HuggingFace Datasets Authors.
	# Copyright 2023 Yuan He.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# TODO: Address all TODOs and remove all explanatory comments
	"""OntoLAMA Dataset Loading Script"""


	import csv
	import json
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{he2023language,
	title={Language Model Analysis for Ontology Subsumption Inference},
	author={He, Yuan and Chen, Jiaoyan and Jim{\'e}nez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian},
	booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
	year={2023}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	OntoLAMA: LAnguage Model Analysis datasets for Ontology Subsumption Inference.
	"""

	_URL = lambda name: f"https://zenodo.org/record/7700458/files/{name}.zip?download=1"

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://krr-oxford.github.io/DeepOnto/"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "Apache License, Version 2.0"


	# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
	class OntoLAMA(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.0.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="bimnli", version=VERSION, description="BiMNLI dataset created from the MNLI dataset."
	),
	datasets.BuilderConfig(
	name="schemaorg-atomic-SI",
	version=VERSION,
	description="Atomic SI dataset created from the Schema.org Ontology.",
	),
	datasets.BuilderConfig(
	name="doid-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Disease Ontology."
	),
	datasets.BuilderConfig(
	name="foodon-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Food Ontology."
	),
	datasets.BuilderConfig(
	name="foodon-complex-SI", version=VERSION, description="Complex SI dataset created from the Gene Ontology."
	),
	datasets.BuilderConfig(
	name="go-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Gene Ontology."
	),
	datasets.BuilderConfig(
	name="go-complex-SI", version=VERSION, description="Complex SI dataset created from the Gene Ontology."
	),
	]

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	if "atomic-SI" in self.config.name: # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"v_sub_concept": datasets.Value("string"),
	"v_super_concept": datasets.Value("string"),
	"label": datasets.ClassLabel(
	num_classes=2, names=["negative_subsumption", "positive_subsumption"], names_file=None, id=None
	),
	"axiom": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	)
	elif (
	"complex-SI" in self.config.name
	): # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"v_sub_concept": datasets.Value("string"),
	"v_super_concept": datasets.Value("string"),
	"label": datasets.ClassLabel(
	num_classes=2, names=["negative_subsumption", "positive_subsumption"], names_file=None, id=None
	),
	"axiom": datasets.Value("string"),
	"anchor_axiom": datasets.Value("string") # the equivalence axiom used as anchor
	# These are the features of your dataset like images, labels ...
	}
	)
	elif self.config.name == "bimnli":
	features = datasets.Features(
	{
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.ClassLabel(
	num_classes=2, names=["contradiction", "entailment"], names_file=None, id=None
	),
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	urls = _URL(self.config.name)
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, self.config.name, "train.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, self.config.name, "dev.jsonl"),
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, "test.jsonl"), "split": "test"},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	if "atomic-SI" in self.config.name:
	# Yields examples as (key, example) tuples
	yield key, {
	"v_sub_concept": data["v_sub_concept"],
	"v_super_concept": data["v_super_concept"],
	"label": data["label"],
	"axiom": data["axiom"],
	}
	elif "complex-SI" in self.config.name:
	yield key, {
	"v_sub_concept": data["v_sub_concept"],
	"v_super_concept": data["v_super_concept"],
	"label": data["label"],
	"axiom": data["axiom"],
	"anchor_axiom": data["anchor_axiom"],
	}
	elif self.config.name == "bimnli":
	yield key, {
	"premise": data["premise"],
	"hypothesis": data["hypothesis"],
	"label": data["label"],
	}